Behavioral Design
A New Approach to Development
Policy
Saugato Datta, ideas42
Sendhil Mullainathan, Harvard University
Abstract
Center for Global Development
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the terms of the Creative Commons
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license.
Successful development programs rely on
people to behave and choose in certain
ways, and behavioral economics helps us
understand why people behave and choose
as they do. Approaching problems in
development using behavioral economics
thus leads to better diagnosis, which in
turn leads to better-designed solutions.
This paper sketches how to use behavioral
insights to design development programs
and policies. It distills the key insights
of behavioral economics into a simple
framework about the constraints under
which people make decisions. It then
shows how this framework leads to a set of
behavioral design principles whose use can
improve the reach and effectiveness of a
variety of development programs.
Saugato Datta and Sendhil Mullainathan. 2012. "Behavioral Design: A New Approach to Development
Policy." CGD Policy Paper 016. Washington DC: Center for Global Development.
http://www.cgdev.org/content/publications/detail/1426679
CGD is grateful for contributions from the UK Department for International Development and the
William and Flora Hewlett Foundation in support of this work.
CGD Policy Paper 016
November 2012
Contents
1. From Evaluation to Design ........................................................................................................ 1
1.1 The Evaluation Revolution .................................................................................................. 1
1.2 The Behavioral Revolution .................................................................................................. 1
1.3 Changing Development Programs ..................................................................................... 2
2. An Example: The Behavioral Economics of Fertilizer Use .................................................. 3
2.1 The usual explanations ......................................................................................................... 3
2.2 The behavioral approach: a different diagnosis ................................................................ 4
2.3 Behavioral Solutions ............................................................................................................. 5
3. The Other Limited Resource ..................................................................................................... 7
3.1 Scarcity of Self-Control ........................................................................................................ 7
3.2 Scarcity of Attention ............................................................................................................. 9
3.3 Scarcity of Cognitive Capacity ........................................................................................... 12
3.4 Scarcity of Understanding .................................................................................................. 14
4. Behavioral Design ...................................................................................................................... 15
4.1 Finding the Behavioral Bottleneck: Diagnosis ............................................................... 15
4.2 Behavioral design principles .............................................................................................. 16
4.3 Testing and Re-design ........................................................................................................ 26
5. Conclusion .................................................................................................................................. 26
References........................................................................................................................................ 29
1. From Evaluation to Design
1.1 The Evaluation Revolution
The last two decades have been exciting ones for development policy. Scientific advances in
evaluation—often equated with randomized control trials—have sparked enthusiasm and
optimism about tackling the persistent problems of poverty. Policymakers now feel better
equipped to judge whether their policies work and whether there are more effective
alternatives. In theory – and increasingly, in practice – funding can be based on solid
evidence. All is not settled, of course. Debates continue on how these evaluations fit into the
bigger picture. But few would dispute that the conversation has changed. “Does it work?” is
a question everyone asks.
This question leads to a new challenge. After many evaluations, we find that while some
interventions work (surprisingly well), many others do not. What can we learn from the
successes and the failures? And more importantly, how do we go about creating more
interventions that work? If we are putting science and dollars into carefully evaluating
programs, should we also not put science and rigor into designing programs? All this leads to a
new question: “How do we design programs so that they work?”
This question can also be answered with rigor. We think a science of design is beginning to
emerge. For obvious reasons, behavioral economics plays a key role in it. Many interventions
stumble because people do not behave the way we expect. Programs are not taken up,
resources are not spent the way we anticipated and so on. Behavioral economics provides a
better way to understanding human behavior. Better understanding leads to better diagnosis,
which in turn leads to better-designed solutions.
1.2 The Behavioral Revolution
Behavioral economics is itself relatively new. Since the 1970s, psychologists, economists,
brain scientists and many others have come together to better understand behavior. Why do
people choose what they do? What motivates them? These insights have changed the way we
understand age-old economic questions. Within development for example, people have used
them to tackle the question of why the poor stay poor (see Banerjee and Mullainathan 2008;
Bernheim, Ray and Yeltekin 2012). But the insights have not just stayed theoretical. They
have been used—and that is the focus of this paper—to design innovative solutions to
persistent problems.
Behavioral economics is already showing up in policy. Inroads have been made in boosting
retirement savings in the United States. The status quo, psychology argues, has a big effect
on behavior. Instead of having employees check a box to enroll in a savings plan, have them
check a box to not enroll. This small material difference is a large psychological one.
Research shows that it can boost savings by over 40%. US pension policy has changed in
1
response. Firms can now default people into savings and many do. An intractable
problem—getting Americans to save for retirement—has become more manageable. The
UK has gone so far as to form a “Behavioral Insights Team”. This team has experimented
with and discovered cost-effective ways to increase tax compliance and reduce enforcement
costs in the judicial system (UK Cabinet Office 2012). These insights are also changing
regulation. Behavioral economics changes how we think about people’s understanding of
complex contracts (“do you understand your cell phone plan?”) or how they process
complicated information (such as the implications of their credit card bill). The European
Union (see Ciriolo 2011) has used this understanding to alter competition policy. In the
United States this has led to changes in the way credit cards bills must describe the balance
due—no longer reporting only the minimum but also how long it would take to pay off the
balance at that amount (often the answer is “forever”).
All this is possible because behavioral economics drastically changes how we think about
people. It helps us realize that people do not behave as we think they do. The assumptions
we make—sometimes without realizing—when we design programs do not match actual
psychology. Our intuitions—and those in economic models—overlook many of the
important things that make people tick. Some of the insights seem self-evident; for example,
that we have self-control problems. Yet they are often overlooked: we all know we have selfcontrol problems but we forget this fact when we design programs.
1.3 Changing Development Programs
Successful programs rely on people to behave and choose in certain ways. Programs work
better when they are designed to match people’s actual psychology. Development programs
are no exception. For schools to be effective, parents must ensure their children show up.
For vaccination programs to work, parents must bring their children to vaccination stations.
For new inventions—whether insecticide-treated bed-nets, smokeless indoor stoves, or new
seeds—to provide benefits, people must adopt and use them. People’s health depends on the
health care available to them. But for this care to be effective they must also do certain
things. They must make prenatal visits. They must adhere to drug regimens. They must feed
their children effectively. They may need to clean the water they drink. Even programs based
on sound medicine, agronomy or education science can fail if they are designed based on the
wrong psychology.
This paper sketches how to use behavioral insights to design better development policy.
Doing so requires first answering one question. Is there anything special about the
psychology of the poor that makes them different in some way? Recent research suggests an
important way in which there is. The conditions of poverty themselves create additional
psychological burdens (see Mani et al. 2012; Shah, Mullainathan and Shafir 2012). In a few
years, we anticipate (hope) these psychological insights will translate into policy. For now,
though, we focus on the work that has already been done. Many powerful insights and
designs come from focusing on the psychologies that affect all of us, rich or poor. Of course
2
the consequences of these psychologies may be different for the poor than the rich (see for
example Bertrand, Mullainathan and Shafir 2004). But the underlying mechanisms can be
thought of as roughly the same. This is a pragmatic approach. Though incomplete, it points
us to many low hanging policy fruit.
Behavioral economics affects program design in three steps. First, it changes how we diagnose
problems. For example, when we see parents failing to vaccinate their child we may be
tempted to conclude that they do not understand the value of vaccination. Behavioral
economics forces us to consider another possibility: they want to vaccinate, they understand
the benefits, but they don’t get around to doing it. Vaccination may be one of many
behaviors, such as savings or going to the gym, where what we do fails to match up with
what we want to do. Secondly, it changes how we design solutions to problems. In some cases it
may suggest that something as simple as a reminder can have an unreasonable impact on
behavior. In others it may suggest a different way to offset our tendency to plan our
spending poorly. Finally, it changes how we define the scope of the problem. Problems we
overlooked may suddenly become interesting ones to solve. We often focus on access
(“Make sure people get the drugs they need at low cost”). Behavioral economics suggests
important problems that remain even after access is solved (“Make sure that people actually
take the drugs they are given”).
One important idea pervades our approach. Applying behavioral economics is not simply
about intuition or about trial and error. It is a scientific, systematic approach to defining,
diagnosing and designing solutions to problems in many different areas.
Figure 1: The stages of the behavioral design process
DEFINE
DIAGNOSE
DESIGN
DEFINED
PROBLEM
ACTIONABLE
BOTTLENECKS
TEST
SCALABLE
INTERVENTION
2. An Example: The Behavioral Economics of Fertilizer Use
Working through a development problem will illustrate each of these steps. Many farmers in
Sub-Saharan Africa use little or no fertilizer on their fields. This might explain why African
crop yields lag behind those in Asia, where fertilizer use is higher (Morris, Kelly, Kopicki and
Byerlee 2007). But why is fertilizer use in Africa so low?
2.1 The usual explanations
How do we diagnose this problem? One possible reason is that fertilizer is not easily
available. Another is that fertilizer is too expensive. Maybe fertilizer does not work as well on
real fields in real conditions as it does on test farms. Finally, it is possible that fertilizer does
3
in
ncrease yield, but
b that farmeers do not kno
ow about this. Each of these diagnoses iss based on
a presumption. The economiic approach leeads us to assuume that a farrmer who does not use
ot want to use any. We impuute intentions from actions. When someo
one fails to
feertilizer did no
do
o something we
w assume theey were unwillling or incapabble of doing itt. So we try to
o
un
nderstand whyy they cannot (fertilizer is not
n available) oor do not wannt to (they do not
un
nderstand the returns). Theese questions, this mindset, in turn affect the solutions we try.
We
W focus on acccess. We try to
t make fertiliizer cheaper bby subsidizing it. We try to iinform
faarmers about the
t benefits of fertilizer to make
m
them chhange their miinds about usiing it.
Figure
F
2: Drop
p-off Betweeen Intention and
a Action
2.2 The behavioral
b
approach:
a
a different diagnosis
These
T
are not bad
b questions to ask, of couurse. The misttake is the othher questions w
we fail to
assk.
In
n the case of fertilizer,
f
the questions
q
we do
d ask may noot lead far. Ferrtilizer fails to be used
evven when it is readily availab
ble such as in small towns aacross Africa. It is sold in sm
mall
am
mounts that evven small farm
mers can afforrd (see Duflo, Kremer and R
Robinson 20111).
Experiments
E
sh
how that fertillizer increases output underr real conditioons on real fiellds: for
exxample, it raises net incomees of maize farrmers in Westtern Kenya byy as much as 118% per
seeason (see Duuflo et al. 20100). And even farmers
fa
who ddo not use ferttilizer are awarre of its
beenefits (Duflo
o, Kremer and
d Robinson 20011). So fertilizzer is availablee, affordable, effective,
an
nd appreciated
d. But it is stilll not used.
Behavioral econ
nomics leads us
u to a very diifferent questiion. Our intenntions do not always
trranslate into acctions. Sometiimes we want to do things bbut do not doo them. Many of us
4
wake up later and go less often to the gym than we would like to. What if farmers have the
same problem with fertilizer? In fact, the farmers themselves agree with this sentiment.
About 97% of Kenyan farmers surveyed by Duflo, Kremer and Robinson (2011) said they
intended to use fertilizer on their fields the following season, but only 37% actually ended up
using fertilizer. Asking the kinds of diagnostic questions economists do not usually ask leads
to some interesting data. These data do not prove the case. And we must tread carefully with
such casual questions; after all people may simply be telling us what we want to hear. But
what is most important here is that a new possibility, a new diagnosis, is added to the mix.
One that has some validity, at least on the face of it: some reason to believe it might be right.
There is of course nothing special about the two-thirds of Kenyan farmers who say they will
use fertilizer and then do not. We all repeatedly fail to live up to our own intentions in big
ways and small. It is merely the case that Kenyan farmers do too. The question is why.
Behavioral economics provides several possible answers. First, we all tend to think we will be
much more willing to do things in the future than we actually are. So we procrastinate repeatedly
about doing even things we want to do. We put things off to a tomorrow that never comes.
In the case of Kenyan farmers, going to the market to get fertilizer is a bit painful, both in
terms of time and money. Farmers plan to “do it tomorrow”, except that the planting season
arrives before “tomorrow” does (Duflo, Kremer and Robinson 2011).
Secondly, we all lack of self-control. We succumb to immediate temptations. Tomorrow we
plan to cut back on sweets. When tomorrow becomes today we eat dessert. This is as true of
farmers in Africa as it is of us. When the farmer is rich with cash at harvest they want to
spend the money on fertilizer. By the time planting time arrives the money is no longer there
(see Brune et al. 2011). The time between harvest and planting is a time when fertilizer can
be overlooked. Money is spent on other things and farmers find themselves caught short
later, when they need the money for farm inputs.
2.3 Behavioral Solutions
A new diagnosis can lead to new solutions. If it is the small hassle cost of travel to town that
leads to farmers procrastinating in buying fertilizer then home delivery early in the season
should help raise use. Duflo, Kremer and Robinson (2011) test this idea and find home
delivery raises fertilizer use by 70%. Think of how interesting and surprising this is: the
alleviation of a small cost can have a large effect. In this case it is because the small cost was
the snag that was causing procrastination.
5
Figure 3: Commitment Accounts in Malawi
18,000
Inputs purchased (MWK)
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Control
Treatment
Farmers who received commitment accounts purchased more in puts than
those in control (Brune et al. 2011)
Similarly, if farmers have self-control problems, we can give them a way to tie their own
hands. They already want to save for fertilizer at harvest. We merely need a way to help them
translate this intention into action: some way to keep the money “safe” (from themselves)
during the long period between harvest and planting. Suppose they had a special account that
let them lock up some of their money, and free it for use at a time of their own choosing.
This simple product works. Farmers given this option bought and used much more fertilizer
and other inputs, leading to higher crop sales (Brune et al. 2011). We are not locking up their
money. They are choosing to. It was not information or fertilizer that was lacking. But there
was no financial product that let them follow through on their desire to use fertilizer.
The fertilizer example shows a few lessons. First, we can often be blind to some diagnoses.
Some snags simply seem too small: the cost of travelling to a nearby town, or the hassle of
remembering to put some money aside soon after the harvest. Yet in some cases—as here—
these overlooked diagnoses can be a big part of the story. Overlook them and we forgo a
potentially powerful solution.
Second, the interventions are so powerful precisely because the snags are “small”. A large
increase in income from using fertilizer can come from quite unexpected places. For
early home delivery amounts to a 10% discount on the market price of fertilizer, but it
increases use by as much (70%) as a 50% subsidy would. Finally, these examples show the
breadth of psychology. It is not merely about marketing or better tools of persuasion
(sometimes it is that too). It is a deeper perspective on what makes people behave.
6
Sometimes we can change behavior without ever changing people’s minds. In this case it is
because they (or many of them) were already convinced.
3. The Other Limited Resource
How do we incorporate the insights of behavioral economics to other problems beyond
fertilizer use? Procrastination and self-control are just some potential psychological
phenomena. What about the others? How do we do diagnose more systematically?
To help navigate the large set of findings, we condense the behavioral literature using one
simple perspective about the constraints under which people make decisions. Economists and
policymakers – indeed all of us – understand constraints all too well. Resources are limited:
there is only so much money, time, staff, or even enthusiasm to go around. Yet we often do
not realize that mental resources are also limited (Thaler and Mullainathan 2000). While we
understand that physical resources must be carefully doled out, we are often blind to our
finite mental resources. Without realizing, we often design programs assuming that people
have unbounded cognitive capacity. We assume that they can think through complex
problems effortlessly and quickly arrive at the “correct” choice. We often assume unbounded
self-control, which leads us to expect people to always resist temptations and do what they
intend to do. These assumptions are often unstated, implicit, or even unconscious, but they
show up in the way we design programs and policies.
Behavioral economics can be understood as identifying a few more limited resources. In
practice, we have found it helpful to think about the limits on four basic mental resources,
each of which we discuss below. For each, we will look at one problem. Then we will show a
few other problems where thinking about each limited resource leads to new ideas about why
problems occur and how we can solve them.
3.1 Scarcity of Self-Control
3.1.1 Labor productivity in developing countries
Much of development depends on labor productivity. People’s productivity affects their
income, a village’s overall level of output and firms’ profits. Many programs focus on
improving productivity. Though these programs are diverse they are based on a common set
of diagnoses. Some programs focus on improving skills. They diagnose low productivity as a
capacity issue: “people are not capable of working more effectively”. Others focus on
incentives, for example motivating teachers to show up at school. These diagnose low
productivity as one of motivation: “people have no interest in working harder”.
These diagnoses miss another possibility. Clearly, workers frequently work less hard than
their employers would like. But is it possible that they also work less hard than they
themselves want to, because they have self-control problems like the rest of us? Self-control
is very hard, as anyone knows who has tried to stick to a diet in the face of a tempting but
7
“forbidden” dish. In the classic “marshmallow experiments,” children left alone in a room
with a single marshmallow struggle visibly as they try to resist eating it in order to win a
second marshmallow as a reward for their forbearance. Exerting self-control is
physiologically effortful, leading to a faster pulse and decreased skin conductance (see
Kahneman 2011). We now know that it makes sense to think of self-control as a psychic
“commodity” of which we have a limited stock (see Vohs et al. 2008), so that using some up
for one task (“continuing to exercise when you want to stop”) depletes the amount available
for other tasks (“resisting the extra cookie after your workout”).
Once we are attuned to the difficulty of self-control, we see work in a different way: as a
series of tests of self-control. It takes self-control to identify, plan and execute all the tasks
that need to be done, all the while resisting the many temptations and distractions that
surround us. All of us sometimes lose some of these battles of self-control, resulting in our
working less hard than we ourselves would like to. Kaur, Kremer and Mullainathan (2011) thought
it plausible that this was happening in an Indian data-entry company. They designed and
offered workers a “negative bonus” scheme. Under this scheme, workers were paid their
usual piece rate if they met self-chosen data entry targets but penalized if they did not. About 35%
of workers chose to set non-zero targets, suggesting that they wanted to get themselves to
work harder. These “negative bonus contracts” increased output by an amount that was
equivalent to the effect of increasing their piece rate by 33%, and by more than a year’s worth of
education. Once again, a behavioral diagnosis led to a large impact: so large that it would have
required raising wages by one-third. And the increase came from an unexpected source: not
paying workers more, or training them afresh, but simply by giving workers a way to work as
hard as they wanted to.
This insight could help solve other problems too. Policies to tackle high levels of
absenteeism among public service providers (such as nurses and teachers) in developing
countries usually rely on enforcement and monitoring. But if self-control problems among
such workers prevent them from working as hard or showing up as often as they themselves
would like, finding ways to mitigate self-control problems may lead to effective alternative
solutions.
3.1.2 Self-Control and problems in farming and saving
When we think about how to increase output on farms, we usually think about how to
increase the adoption and use of inputs like fertilizer or improved seeds. These are clearly
important. But there are other important behaviors that affect how productive farms are. For
example, farmers weed much less than they should. For example, Banik et al (2006) find that
weeding twice a season instead of once raises yields in India by 23% for wheat and an
enormous 49% for chickpeas.
But why do farmers weed so little? We usually diagnose the problem as arising from a lack of
knowledge: farmers may just not know how important weeding is. But weeding is also timeconsuming, easy to postpone, and tedious. In sum, doing it requires self-control. So do other
8
things farmers do not do enough of, such as channeling runoff water correctly. So behavioral
economics suggests a different diagnosis, one of self-control problems. Once we recognize
the role of self-control in agricultural work, we see many ways to increase productivity on
farms beyond increasing input use.
Similarly, when we see low savings rates, we usually assume that people do not want to save,
or that savings programs are not lucrative enough. As a result, we try to raise awareness
about the importance of saving or to make saving more financially rewarding, whether by
increasing interest rates or matching contributions. But building up savings is a process with
many steps, several of which demand self-control. It requires self-control to not spend
money when it is available, but also to make and stick to plans to go to the bank and deposit
it. Recognizing the role of self-control in making it hard for people to save as much as they
want led behavioral economists to design a special savings account for clients of a bank in
the Philippines. These accounts allowed them lock up funds in their own accounts until a
self-specified goal had been reached. Nearly 30% of the clients who were offered such a
restrictive account opened one, and the effect on the savings balance after one year was an
81% increase (Ashraf, Karlan and Yin 2006). Achieving an increase of this magnitude would
have required an unfeasibly large increase in the interest rate offered. Once more, we see
how small behavioral interventions can have almost unreasonably large effects.
3.2 Scarcity of Attention
3.2.1 Technology adoption in developing countries
Adopting and becoming proficient at using new machines, inputs or techniques of
production is a big part of the development process. Yet technology adoption can be
frustratingly slow. Many beneficial techniques are not adopted at all, and workers in
developing countries often use newer machines and techniques less efficiently than possible
long after they get access to them. To speed up the process, governments often use
programs (such as agricultural extension programs) intended to teach potential users about
the benefits of new technologies and how to use them correctly. These programs vary in
their details, but they all rest on a common diagnosis: a lack of knowledge about how to use
technology. Yet mastering a new technology often requires more than just knowing about it
or even a superficial understanding of what it involves. Rather, it requires a user to be
especially attentive to some particular features or aspects of the technology. If he does not pay
attention to the right things, a person is unlikely to become adept at using the new
technology.
This would not matter if people noticed everything, as they would if they had unlimited
attention. But psychologists have found attention to be a limited resource, just as self-control
is. In one famous experiment that has been repeated many times, over half of those tested
fail to notice a gorilla walk across a basketball court because they are paying attention (as
instructed) to the number of passes made by the teams on court. Similarly, subjects in
dichotic listening tests concentrate on one of two distinct audio streams – each emanating
9
from one headphone – and remember very little about even the stream they are asked to pay
attention to and essentially nothing about the other. Attention – both visual and aural – is
even more limited that we usually realize. This leads to a different diagnosis. Because of
limited attention, people are unlikely to notice all aspects of a new technology. And they may
fail to notice precisely those dimensions of a new technology or technique that actually
matter, because these dimensions are ones they are used to ignoring.
A recent study of seaweed farming in Indonesia (see Hanna, Mullainathan and Schwartztein
2012) shows how limited attention can slow down or prevent technology adoption. Seaweed
farming is done using the “bottom method” where the farmers attach “lines” through
wooden stakes driven into the shallow seabed near the shore. Raw seaweed from the
previous harvest is then taken, cut into pods, and attached to these lines. Farmers tend to
these pods when the tide is low, and harvest the seaweed after 30-45 days. A number of
things could affect yield. These include the size of the seaweed pods, the distance between
lines, and the distance between pods on a line. Maximizing production and income involves
figuring out the optimal combination of these dimensions.
It turns out that farmers simply ignore pod size, which experiments have found does matter.
When asked about the length of a typical line, the distance between lines, or the optimal
distance between knots and lines, close to 100% of farmers can answer the question. But
when asked about the current pod size or the optimal pod size, only 16-17% of them are
able to provide an answer. Because seaweed farmers do not think pod size is important, they
pay little attention to it. As a result, they also do not learn over time that it matters: it simply
evades notice.
This diagnosis helps explain why simply providing information does not help. Even
participating in experiments designed to measure the effect of pod size on output has no
effect on farmers adopting the right pod size, although the experiments themselves show
that moving from the worst to the best pod size raises income from seaweed farming by
30%, and total income by 20%. Just like people missed the “gorilla”, farmers are effectively
blind to features of a technology they initially believed did not matter.
3.2.2 Further applications: saving and drug adherence
Behavioral economists have shown that limited attention prevents people from acting on
their own intentions to save. So limited attention – and not the usual diagnoses of the
absence of the desire to save or low returns to saving – may help explain why people save so
little. If so, drawing people’s attention to their own plans to save may help increase savings
rates. In a series of experiments in Peru, Bolivia and the Philippines, behavioral economists
successfully raised the amount people saved simply by providing them with timely reminders
about their own saving goals (see Karlan et al. 2010).
10
Figure
F
4: Textt Message Reeminders Inccrease Saving
gs
In
n the United States,
S
close to
o 100,000 peop
ple have theirr limbs amputaated each yearr because
off complication
ns arising from
m diabetes. Yeet diabetes is eeasily treated, and most of tthe people
who
w lose their limbs
l
have beeen prescribed
d medication ffor diabetes, bbut do not takee it
ondition
reegularly. Similaarly, access to anti-retroviraals has made H
HIV/AIDS a m
manageable co
evven for patien
nts in many developing coun
ntries. But herre, too, many ppeople do nott take their
piills regularly. Tuberculosis
T
has
h long been treatable if paatients compleete a course of
medications
m
– but
b many do not.
n In all thesse cases, not ddoing somethiing trivially sm
mall –
taaking one’s meedicines – has almost unimaaginably damaaging outcomees – the loss o
of a limb or
evven death.
Because the consequences off not taking medicines
m
are sso big, we usuually assume a big gap in
nowledge or understanding
u
g must be resp
ponsible. As a result, we typically focus on
n
kn
ed
ducational or communicatio
on campaigns about the impportance of taaking one’s meedicines.
Yet
Y behavioral economics suuggests that wee might be miissing a plausibble alternativee diagnosis:
in
nattention. Wh
hat if people simply forget to
t take their ppills day after dday? Many illn
nesses –
in
ncluding diabeetes and tubercculosis after a certain stage – are not sym
mptomatic most of the
tim
me, so it is all too easy to fo
orget to take medication.
m
Thhis insight seeems implausib
ble, because
it identifies som
mething triviall – forgetting – as leading too something hhuge – such ass a loss of
liffe. Yet solutio
ons to the prob
blem of forgetting have prooven very effeective at raisingg drug
ad
dherence. In the
t United Staates, GlowCap
ps - pill bottless that light up if not openedd at the
right time – havve dramaticallly raised adherrence. In Soutth Africa, simpple text messaage
o increase adheerence.
reeminders havee been used to
11
3.3 Scarcity of Cognitive Capacity
3.3.1 Ineffective financial literacy programs
Running a small business involves a lot of financial management, ranging from keeping
business accounts to managing debt. Yet many of those who run small businesses, in both
developing and developed countries, are not financially literate enough to handle these tasks,
causing their businesses to suffer and making it difficult for them to grow. In response,
agencies and governments have developed financial literacy training programs for small
business owners. However, there is little evidence that financial literacy training makes a
difference to how small businesses are run and how their owners manage their finances. For
example, Drexler et al. (2011) evaluate a standard financial literacy program in the
Dominican Republic and find that it has no impact on how entrepreneurs manage their
finances, and consequently none on how profitable their businesses are.
This low impact is typically diagnosed as arising either from a lack of interest and motivation
on the part of those who attend financial literacy trainings or from not enough useful
material being covered in training sessions. So we often see interventions that aim to
increase the number of sessions people attend, or to make the material covered in financial
literacy training more comprehensive. Yet even these interventions have proven
unsuccessful.
Behavioral economics can help us understand why. When we try to make financial literacy
curricula more comprehensive and rigorous, we are assuming that people can process large
amounts of complex information quickly and effortlessly. Yet research shows that this is
simply not true. The cognitive resources available to people at any moment are limited and
can be depleted by being used for other activities. So increasing the cognitive demands of
financial literacy programs may in fact be making them less likely to succeed. These solutions
target the wrong psychology.
On the other hand, behavioral economists have found that bounds on cognitive and
computational ability lead us to “economize” on cognition while making decisions.
Wherever possible, we use fast, intuitive thinking or rough rules of thumb. An alternative
way to try and improve outcomes from financial literacy programs is therefore to build them
around simple rules of thumb of the kind that people actually use. A test of such a “rules-ofthumb”-based financial education program in the Dominican Republic found large increases
in the adoption and use of good financial practices (such as separating business and personal
accounts) as well as a jump in profitability (Drexler et al. 2011). Building solutions around
psychology makes them more likely to succeed.
3.3.2 Further applications: Pension and Cash Transfer Programs
Many governments in low- and middle-income countries are concerned with getting more
people to participate in pension systems and save for their old age. Yet few participate and
those who do join programs often contribute very little. Governments typically diagnose the
12
problem as arising from a combination of a lack of interest in saving for the future and the
savings programs themselves not meeting people’s needs. So they try to make saving
programs more attractive. One way in which they do this is by providing people with more
choices about how much they can contribute, where their money is invested, and so on.
Yet behavioral economists points out that more choice is cognitively taxing, and giving
people too many choices may overwhelm them. As a result, even those who want to save
may end up not doing so because they find it too hard to choose between the many plans
and rates available. Providing more options can actually make it less likely that any of them is
chosen.
Thinking about choices in this way leads to very different solutions, such as simplifying plans
and reducing the number of dimensions that people have to compare, or picking an option
that people are automatically defaulted into. Choice simplification can be remarkably
powerful. A study of loan take-up in South Africa found that reducing the number of
combinations of interest rates and loan tenures led to as much of an increase in uptake as
reducing the interest rate charged by 2.3 percentage points (Bertrand et al. 2010).
Figure 5: Behavioral Manipulations Increase Loan Take-Up
Behavioral Manipulation
Reduction in Interest Rate
Required for Same Effect on
Take‐up
Reducing number of loan options
from 4 to 1
2.3 percentage points
Using female picture for male
applicants
3.2 percentage points
Behavioral manipulations increased loan applications in South Africa by as much as large
reductions in the interest rate (Bertrand et al. 2010)
We also often diagnose the problem of low uptake of programs to a lack of interest in the
program among those targeted, and respond by providing more information. Yet if cognitive
space is limited, a flood of information may simply overwhelm people and could actually
reduce uptake. In addition, scarcity of cognitive resources can lead people to attach more
weight than expected to features of the way that information is presented, such as whether
something is couched as a gain or a loss. Once we know that cognitive biases are at work,
however, we can design communication and advertising to remove these biases or to take
advantage of beneficial ones. Behavioral economics leads us to pay attention to features of
communication – how changes are framed, how many options are presented, and how
complex choices are – that we might otherwise dismiss as unimportant.
13
3.4 Scarcity of Understanding
3.4.1 Under-use of Oral Rehydration Solution
Over half a million infants throughout the developing world – around 150,000 of them in
India alone – die of diarrhea each year. Yet by some estimates, over 90% of these deaths
could be easily averted through the use of a balanced solution of salts known as Oral
Rehydration Solution (ORS). Why is ORS not used enough?
Most attempts to solve the this problem diagnose it as arising either because people do not
know about ORS and how it works, or because it is not readily available or affordable. We
therefore try to make sure that ORS is cheap and easy to find by working on distribution and
cost, while also using advertising and other kinds of awareness campaigns to inform mothers
about its benefits.
However, ORS use remains low even where it is available and cheap. Behavioral economics
helps us understand why. In making decisions – such as what to do when a child has
diarrhea – we have to rely on an underlying theory, possibly an unstated or implicit one,
about the disease and its control. This constitutes our “mental model” of the world. Usually,
we assume that these underlying theories are broadly correct. But behavioral economists
argue that this understanding, too, is scarce; not all underlying causal relationships are
correctly or accurately understood.
A child with diarrhea is constantly leaking fluids. Given this, a perfectly plausible mental
model of the disease would imply that putting any more liquids into the child will only make
it sicker; keeping the child “dry” is better. Indeed, when poor women in India are asked
whether the solution to a child with diarrhea is to increase or decrease its fluid intake, 35%50% say that the answer is to decrease it. But with this mental model of the disease, it will
never make sense to use ORS – and unless this model is somehow overturned, ORS will not
be tried. This helps explain why many mothers do not use ORS despite its being cheap,
readily available and well known. It also suggests that any effective solution will have to
tackle the flawed mental model at its root: without doing so, information or exhortation is
unlikely to have much effect.
3.4.2 Further applications: Imbalanced Fertilizer Use and Schooling
decisions
Rice farmers in some parts of India over-use nitrogenous fertilizer. Usually, we think that
this is the result of poor pricing policies and a lack of awareness about the right ratio
between various kinds of fertilizers. This has led governments to concentrate on
disseminating information on the right way to use fertilizer through extension offices or
information campaigns. However, behavioral economists argue that a flawed mental model
is at work here. Farmers are used to extrapolating the likely yield of a crop by looking at the
extent of its green leafy growth: “green = healthy”). Nitrogenous fertilizer is good for such
leafy growth. In the case of many crops, (like spinach), this provides a good rule of thumb.
14
But in the case of grains, too much leafy growth can detract from yield. Relying on this
otherwise useful mental model can thus lead farmers to use too much nitrogenous fertilizer.
Similarly, some poor parents pull their children, or some of their children, out of school after
only a few years of schooling. The standard diagnosis of this problem is that parents lack
interest in schooling or that schooling is too expensive. As a result, many governments have
adopted policies involving reducing or eliminating school fees or providing financial
assistance to poor parents as ways of increasing schooling among the poor.
However, there is evidence that flawed mental models may play a role here too. Data from
the developing world shows that each additional year of schooling adds roughly as much to
earning power as the previous one. Yet parents in both Madagascar and Morocco strongly
believe that primary school is about half as valuable as secondary school, which is about half
as valuable again as high school (Banerjee and Duflo 2011). Many parents therefore think of
schooling as essentially worthless unless they can afford to send their children all the way
through high school. So they may allow their children to drop out with even less education
than they can afford to pay for. Indeed, just telling parents in Madagascar about the average
income gains from spending one more year in school for children from backgrounds like
their own increased test scores for their children, and was particularly effective for children
whose parents had earlier under-estimated the return to education (Banerjee and Duflo
2011).
4. Behavioral Design
Unlocking the potential of behavioral economics requires a systematic approach to design.
We break the design problem into four parts – problem definition, diagnosis, design, and
testing. Here, we focus on the two central parts of this scheme: diagnosis – where to intervene
and design—how to intervene.
4.1 Finding the Behavioral Bottleneck: Diagnosis
The first step to finding a solution is identifying the root of the problem. Where are things
going astray? We may be tempted to identify bottlenecks intuitively, but our intuitions about
psychology can be fallible. In addition, there are usually many psychological barriers or
phenomena that could be at play in a given situation. Guessing and eliminating using a series
of experiments is thus likely to be both unreliable and expensive. We instead need a
systematic approach to identifying candidate bottlenecks. This process, which we call
“behavioral mapping,” reveals “behavioral stress points,” each of which is a possible
intervention point.
Behavioral mapping begins with a problem. For instance, it might be that farmers apply too
little fertilizer to their fields. It then decomposes this behavior into a number of parts. Did
the farmer ever intend to buy the fertilizer at some point? If he did not, the bottleneck is at
the point of decision. If he did, then the bottleneck is one of action—of following through
15
on a decision—or of belief revision—sticking to the original decision. Figuring out which of
these is most relevant requires us to use further diagnostic tools. Did the farmer continue to
believe for long periods that he would use fertilizer tomorrow, just not now? Did the farmer
simply forget? Note that each of these diagnostic questions is derived from a psychology we
described above: the first looks for self-control problems, whereas the second is linked to
limited attention.
There is, however, no mechanical, one-to-one mapping between a psychology and
bottlenecks. For example, scarcity of self-control could imply bottlenecks at several stages. A
farmer lacking self-control may not save the money to buy fertilizer. Or he might not take
the effort to figure out if fertilizer is right for his farm – so that he never gets to the point of
trying to save to buy it. Behavioral mapping is thus a process that generates questions; these
then lead to surveys—qualitative and quantitative. The data from these surveys then guide
the next set of questions. A careful use of data and observation then allows us to arrive at a
shortlist of the most important among the hypothesized bottlenecks mapping has helped
shortlist. These bottlenecks eventually feed into designs, which are then tested.
4.2 Behavioral design principles
The design phase – deciding how to intervene in a particular situation – follows this diagnosis.
The designs obviously depend on the hypothesized bottlenecks. Solving a problem of
forgetting makes little sense if the person never intended to take the action. However, once a
relevant behavioral bottleneck has been identified, the set of design principles we describe
below can guide behavioral design. Of course, these principles need to be applied
judiciously. But they give a clear sense of how behavioral insights can improve the design of
policies and interventions once we have honed in on the right bottlenecks and the reasons
for them.
Principle 1: Facilitate Self-control by Employing Commitment Devices
As we have seen, we all have self-control problems. We may decide to do one thing but then
find it challenging to follow through. We also often seek ways to stick to our own plans.
Signing up for a gym membership is one way to encourage our future selves to go to the
gym. So is the more aggressive (and more expensive) decision to sign up for a training
session.
Interventions or suitably designed products can affect this imbalance between intention and
action. Clocky is an alarm clock that makes it easier to stick to plans to wake up early. When
you hit its snooze button, Clocky jumps off the nightstand and rolls into a corner. When it
goes off again, you have no choice but to get up. Similarly, a non-profit called StickK allows
people to stake their own money on things they are trying to get themselves to do, such as
give up smoking. Their funds are only returned to them if they are certified as having met
their goal by a third party. So far, nearly 150,000 “commitment contracts” have been taken
out on www.StickK.com.
16
Poor people in the developing world suffer from the same self-control problems as the rest
of us. They also try to find workarounds that make it easier for them to stick to their
intentions. For example, poor women in developing countries often “borrow to save” by
taking a loan from a microfinance institution (MFI) and then place that loan into a savings
vehicle. The point of doing this is that the need to pay the MFI back provides the discipline
needed to save regularly, which is otherwise not available. Borrowing to save thus results in
savings while saving on your own may result in nothing.
Policy design can incorporate these insights more explicitly. We saw this for savings in the
Philippines (Section 3.1). Similarly, farmers in Malawi used restrictive accounts to get around
their own tendency to spend harvest proceeds too quickly, which resulted in a cash crunch
and under-investment in the next agricultural season (see Section 2.4). In both cases, the
saver valued restrictions on future behavior.
There are many further applications. For instance, poor people in developing countries often
take high-interest loans for predictable expenses they could easily save up for, such as for
home repairs, appliances, school fees or medical expenses related to childbirth. Low- to
middle-income women in rural India take on large debts to pay for the costs associated with
delivering a child in hospital. An ongoing experiment in rural Andhra Pradesh, India, allows
pregnant women and their families to save regularly in a designated account. The saved
amount cannot be withdrawn until they give birth, but forms the basis of a loan to cover
childbirth-related expenses. Take-up of this product in the pilot area suggests that it is
fulfilling a deeply felt but unmet need.
Such commitment devices could help people in some seemingly unlikely situations. As
discussed above, Kaur et al. (2011) find that data-entry workers in India enthusiastically
adopt a payment scheme that essentially penalizes them for not hitting targets. This idea
could be applied to other situations where workers don’t work as hard as they themselves
would like to, such as in factories or other informal work environments where workers are
paid piece-rates but are unable to effectively monitor their own pace of work. Such
commitment contracts could also help tackle the widespread problem of absenteeism among
public-sector workers in developing countries such as India, where 25% of government
schoolteachers are absent from work on any given day, or Uganda, where 27% are missing
(Chaudhury et al. 2006).
Commitment devices are not the only way to help with self-control problems. We can also
resolve the problem by allowing people to act on their good intentions at the moment they have
them. One solution to the problem of low savings, for example, is to develop products that
turn saving into an “impulse purchase” by making it possible to buy savings at the store just
as one might buy other products. One version of this idea, currently being tested in India by
ideas42, involves selling stored-value cards such as the ones people routinely use to top up
their mobile phones, except that the money spent on the “savings card” adds to a person’s
bank balance rather than his mobile air-time.
17
Principle 2: Reduce the Need for Self-control
It is true that we all have self-control problems. But it is also true that these problems are
sometimes unintentionally created or made worse by the way policies are designed. A second
way to tackle problems caused by a lack of self-control is therefore to find ways to reduce
the need for people to exert self-control.
For example, poor families in the United States receive food stamps – transfers that enable
them to buy food – at the beginning of the month. This system was designed for
administrative convenience. But it worsens the effect of self-control problems: people overspend in the first part of the month and are left with too little money for food by the end
(see Gennetian et al. 2011). Switching from a single monthly installment to two fortnightly
or four weekly ones in the case of food stamps would dramatically reduce the need for
recipients to exert self-control in the first place.
Farmers face an even more challenging self-control problem. Harvest incomes come in once
a season, sometimes once a year. Mani et. al. (2012) show that sugarcane farmers for
example pawn jewelry and consume a lot less in the months before harvest than in the
months after harvest. This need not be the case. Some of the harvest income could easily be
paid into an account that disburses a steady stream of monthly or fortnightly income instead
of the one-off payout that is now common. The self-control problem is not inherent to the
situation: changing the payout structure can rectify it.
Disaster-relief or other forms of compensation (such as compensation for being displaced by
infrastructure or other projects) are also usually paid out in a single lump-sum. This imposes
enormous self-control burdens on people who receive them. Switching to paying such
benefits as a stream of payments over time rather than a single payment would mitigate these
problems.
Developing countries, many of which are introducing or expanding cash transfers to the
poor, can use these ideas to create more effective cash transfer programs than those in use in
developed countries. For instance, income support programs for agricultural workers ought
to be heavily loaded towards paying out in the agriculturally lean seasons. Using mobilebased or electronic methods of moving money allows the incorporation of more frequent
transfers from the outset. This will help them avoid many of the problems which programs
in the United States (such as the food stamps program discussed above) or Europe are only
now beginning to try and rectify.
In many countries school fees and associated expenses are due at the beginning of a school
year in one lump sum. This imposes self-control burdens, because many poorer parents
cannot pay the entire year’s fee out of their current income and so need to save up for it
over time. Changing the timing of such fee demands to line up with the timing of income
flows can increase people’s ability to pay them. This problem is particularly marked for those
with seasonal incomes (such as agricultural workers) whose income flows may not line up
18
with the timing of such large payments. Switching from one large lump sum to allowing
installment payments would also reduce the self-control burden.
Principle 3: Remove Snags to Choosing
We tend to assume that people make active decisions: faced with a set of options, they
always actively choose the one they like most. However, behavioral economists have found
that people frequently passively accept whatever happens if they do nothing. This means that
the “default option” is disproportionately important. Similarly, seemingly trivial steps,
decisions and choices (a form to fill or the need to submit one that has already been filled,
for example) can drastically reduce the number of people who participate in a program. All
this means that program uptake and use increases dramatically when the default is changed,
or when a program is re-designed to reduce the number of things people have to do to take
advantage of it.
In Morocco, Devoto et al. (2011) find that nearly 70% of households who were helped with
the administrative steps needed to get a piped water connection signed up for piped water,
compared with just 10% of those who were not. Reducing the hassle of participating in
programs can thus have dramatic effects on how many people they reach.
Making things automatic also helps. Automatic transfers into savings accounts can increase
saving rates by removing the small steps that stand between an intention to save and actual
savings. Such automated transfers can be used to help people make the most of many kinds
of income or transfer flows. Depositing a fraction of a benefit or crop payment into a
savings account is far more likely to allow a farmer to avoid a cash shortage before the next
payment comes in than allowing all of it to be close at hand. Workers in small or mid-sized
firms in developing countries would benefit from having some of their income automatically
put into savings accounts. Automation uses choice inertia – so often a source of forgone
advantages – to people’s benefit.
The idea of manipulating defaults has revolutionized retirement savings programs in the
United States. A decade ago, when employees in most American companies had to fill out a
form in order to participate in their company’s 401(k) program and avail of the employer’s
matching contributions, participation in 401(k)s was low. However, flipping the default
option around from exclusion (i.e. having to actively “opt in”) to inclusion (i.e. having to
“opt out” if you don’t want to participate) raised participation in the retirement plan of the
first test company from 37.4% to over 85% (Madrian and Shea 2001). This has been hugely
influential: by 2007, over 50% of US employers with 5,000 or more employees used such a
design, up from less than 15% in 1999 (citation?).
19
Figure
F
6: Defaaults and 4011(k) Participaation
A program for fortifying flouur with iron, described
d
in B
Banerjee and D
Duflo (2011) shows the
po
ower of this in
nsight in a devvelopment con
ntext. The proogram was designed so thatt a
ho
ousehold had to tell the milller whether itt wanted to haave its flour ennriched only o
once; the
miller
m
was supp
posed to act acccordingly eacch time they ccame back to hhim. Unfortun
nately, the
paarticipating millers
m
flipped this
t around: th
hey required thhe householdd to say whether they
wanted
w
iron added to their fllour each timee they broughtt grain to be m
milled. This ch
hanged
deefault setting was
w enough to
o cause particiipation in the program to pplummet, causing it to
faail to achieve its
i objectives.
Unfortunately,
U
d
are buuilt into a num
mber of featurees of poor peo
ople’s
unfavorable defaults
livves. For exam
mple, we can reely on the water that is pipeed into our hoomes being saffe because
it has already been chlorinateed. The poor on
o the other hhand have to eensure that th
hey use
ch
hlorine tabletss if they want similarly clean
n water. Of coourse, they oftten forget to ddo this – as
we
w would if wee had to every time we filled
d drinking watter – thus frusstrating effortss to reduce
deeaths from diaarrhea and oth
her water-born
ne diseases.
Behavioral econ
nomists therefore argue thaat getting morre people to usse chlorine reqquires
making
m
its use as
a close to auttomatic as posssible. A chlorrine dispenserr installed at th
he village
well,
w dispensingg exactly the right
r
amount of
o chlorine eacch time at thee press of a buutton,
reemoves most of
o the steps th
hat people norrmally have too take to chlorrinate their waater. In
Kenya,
K
Kremerr et al. (2009) found that theese dispenserss were the moost cost-effectiive way to
reeduce diarrheaa that has so far
fa been deviseed or tested.
Siimilarly, manyy nutrition pro
ograms try eith
her to get the poor to eat thhe kinds of foo
od that
naaturally provid
de a balanced mix of micro-- and macro-nnutrients, or too get them to adopt
sp
pecial nutrition
nal supplemen
nts. Most such
h programs haave very little success. Behaavioral
20
science suggests that it might be most effective to make balanced nutrition close to
automatic by fortifying food that people already eat with extra micronutrients, much as the
routine iodization of salt has vastly reduced problems of iodine deficiency.
Principle 4: Use Micro-Incentives
We normally think that a small monetary or material incentive has no chance of inducing a
change with large consequences. However, behavioral economists have found that such
“micro-incentives” affect how people behave in ways that have big consequences for their
own well-being. The size of an incentive only needs to be as large as the barrier that caused
the problem: if this is small, as it so often is, a small incentive is often enough.
For example, taking a child to a free immunization camp is a tiny inconvenience to endure
for the protection provided by a full dose of vaccines. Yet by offering each parent who
brought a child a half-kilo bag of lentils – equivalent to about half a day’s wages for an
agricultural laborer – succeeded in nearly doubling the fraction of children who were fully
immunized in a part of rural India, from 18% to 29% (Duflo et al. 2010). In addition,
because nurses’ wages, equipment costs and other program costs were fixed, adding the
financial incentive actually resulted in halving the cost per child immunized.
In the United States, Volpp et al. (2008) offered patients on warfarin, an anti-stroke
medication, a lottery ticket as a reward for taking their pills. Prior to the incentive, 20% of
the patients were not taking their medication correctly. The opportunity to win a small sum
of money (the highest prize was $100, which an individual had a 0.1% chance of winning),
however, succeeded in virtually eliminating incorrect drug adherence.
In Malawi, only 34% of those getting tested for HIV at government-run testing centers were
returning to pick up their results. Offering a tiny incentive of around $0.15 (i.e. about 10%
of the daily wage) more than doubled the fraction of people who picked up their test results
(Thornton 2008). Giving people a larger incentive – of up to $3 – did have a larger effect,
raising the rate further to over 90%. But the bulk of the jump – from 34% to over 70% - was
achieved simply by moving from no incentive to a tiny incentive.
Wider use of such micro-incentives could dramatically improve policies to control diseases
like tuberculosis and HIV/AIDS (where medication is readily available but adherence is
often a problem) as well as immunization in the developing world. The spread of mobilephone technology opens up the possibility for innovative new ways of delivering such
micro-payments. For instance, governments, mobile phone companies and pharmaceutical
companies could collaborate on finding ways to transfer small amounts of airtime (or mobile
cash) to people if they take their medicines.
21
Figure
F
7: Micrro-Incentivess Increase Im
mmunization
n
Figure
F
8: Micrro-incentivess induced mo
ore people too pick up HIV
V test resultss
22
Small incentives, frequently paid, could be used to reward a variety of “good behaviors” in
education such as school attendance. Recent evidence from the United States suggests that
providing financial rewards for specific actions such as reading books or finishing homework
raises test scores among students from low-income families (see Fryer 2010). Microincentives could also increase take-up of government programs that provide supplemental
nutrition and other kinds of care to poor mothers or pregnant women, increasing the
effectiveness of efforts to tackle problems like low birth weight and malnutrition.
Finally, more and more developing countries are moving to a point where the key constraint
on achieving health and education goals is no longer building schools or clinics but rather
ensuring that education and health workers show up to work. The World Bank estimates
that 25% of Indian schoolteachers, and 19% of teachers in Bangladesh are absent from
school on a given day (Chaudhury et al. 2006). Giving teachers or nurses a small incentive
payment for each day over a certain minimum that they spend working could help tackle
absenteeism, and maximize the impact of investments in health and education infrastructure.
Principle 5: Reduce Inattention: Reminders and Implementation Intentions
Behavioral economists have found that reminders – in person, using a phone call, or via text
message, for example – can have dramatic positive effects on behaviors such as a failure to
get tested for diseases, not taking medicines regularly, or even the tendency to incur penalties
on high-interest borrowings. In all of these cases, following through on an intention requires
a person to remember to take several steps, and it is easy to forget or neglect to do one of
them. But missing a single step often derails the whole process. A timely reminder goes a
long way towards mitigating these problems.
For example, clients of three banks in Bolivia, Peru and the Philippines who were reminded
(via letter or SMS) to make deposits into their accounts saved significantly more and were
also more likely to reach a pre-set savings goal (Karlan et al. 2010). Similarly, Stango and
Zinman (2011) find that having their attention drawn to their bank’s policies about fees for
overdrawn accounts reduces overdraft fees paid by individuals for up to two years after the
reminders were sent.
Reminders have been used to increase workplace productivity. Cadena et al. (2011) found
that reminding loan officers in a Colombian bank about their goals for credit disbursal,
collections, etc. reduced their tendency to postpone contacting potential new clients or
making efforts to collect on outstanding credit till just before their monthly bonuses were
due. As a result, loan officers earned more and the quality of the bank’s loan portfolio
improved.
Encouraging people to make specific plans about when and how they will do something acts
like a reminder, drawing attention to actions that might have been neglected. Such
implementation intentions have been used to successfully encourage a number of healthrelated behaviors in the United States, including a 10% increase in the number of people
making and sticking to appointments to be screened for colon cancer (Milkman et al. 2012).
23
Similarly, Milkman et al. (2011) show that nudging people to form plans about getting an
influenza shot increased the fraction of people who got the flu vaccine.
The scope for applying the broad idea of using reminders and implementation intentions in
developing countries is enormous. The spread of mobile phones makes it feasible to use text
messages or calls to carry out the monitoring needed to ensure adherence to drugs for
communicable diseases, reminding people to take their pills as near as possible to the actual
times they need to take them. Timely reminders can be used to tackle other (non-medical)
situations where people forgo significant benefits because they do not do something (such as
weed their fields) at the right time.
Principle 6: Maximize the Impact of Messaging: Framing Effects, Social
Comparisons, Norms
Governments, agencies and non-governmental organizations communicate with the target
audiences using information campaigns, billboards, letters, television or radio
advertisements, and now personalized messaging through phones. Behavioral economics
provides a number of principles about the content and framing of such messaging that can
make it more effective at achieving its desired ends.
For example, messaging that links money with specific goals is an extremely effective way to
increase savings rates. In the study of the effect of reminders on savings rates in Peru,
Bolivia and the Philippines discussed above (Karlan et al. 2010), reminders that emphasized
a specific goal – a house, an appliance purchase, etc. – were twice as effective as ones that did
not. This is because people treat money differently depending on what they think its purpose
is. They are much more likely to hold off from spending a dollar associated with a longerterm goal than a dollar which, in their minds, is intended for general expenses.
People are much more responsive to being informed of what they lose by not doing
something than they are to being told how much it benefits them. This insight can help
refine the design of programs that seek to encourage people to take steps to ensure their
own or their children’s health: emphasizing the possible ill-effects from not vaccinating a
child, for example, may be much more effective than emphasizing how healthy the child will
be if she is vaccinated.
Thirdly, comparing a person to his peers, neighbors, friends, etc. is an extremely effective
way to change behavior. For example, American households who got mailers that compared
their own electricity consumption to that of homes in their own neighborhood reduced their
power consumption by as much as they would have if the cost of power had risen by 11%20%, with effects thrice as large for those who were initially using the most electricity
(Allcott and Mullainathan 2010). Similarly large effects have been found for water
consumption (Ferrarro and Price 2011), where comparison to neighbors curbed water
consumption far more than either simple information provision or messages exhorting
people to be thrifty users of water. Drawing attention to what progressive neighbors are
doing could thus spur the adoption of many beneficial technologies in developing countries.
24
Most individuals make efforts to conform to what they perceive the social norm to be.
Sometimes, though, their perceptions about the norm may be inaccurate: less common
behaviors may be more visible, leading people to think they are in fact “what everyone
does.” Messaging about social norms can change people’s perceptions about what is normal
and thus change behavior. This idea could help tackle a number of important problems in
developing countries. For example, while 25% of Indian teachers and 19% of their
Bangladeshi counterparts are missing from school each day, the fact remains that between
75% and 81% do show up (Chaudhury et al. 2006). Being present is thus the norm, and
emphasizing this may make those who routinely violate this norm less likely to do so. More
girls now attend school than those who do not, even in countries where gender gaps in
education persist. Drawing attention to this could help reduce these gaps further.
Finally, making a particular feature of a person, his environment, or a product more salient
often has large effects on people’s choices. For example, reminding a person (even
inadvertently) of an aspect of their identity induces them to act in ways that fit in with the
stereotypes associated with that aspect (Steele and Aronson 1995). A recent demonstration
of this comes from India, where Hoff and Pandey (2009) gave village schoolchildren a set of
simple puzzles to solve. They found that carrying on the experiment in a way that made it
clear that participants’ caste was known reduced the performance of lower-caste students
dramatically, but did not affect the performance of others. This suggests a need for carefully
examining communications and publicity to ensure that they are not inadvertently
strengthening damaging stereotypes or modifying them to evoke positive associations with
aspects of people’s identity.
Principle 7: Frame Messages to Match Mental Models
Existing mental models sometimes stand in the way of people adopting beneficial
technologies or undertaking profitable investments in physical or human capital (see Section
3.4). For example, farmers who believe that fertilizer has no effect on productivity unless
used in large quantities might forgo the proven effects of using even a small amount. The
poor are also often unduly pessimistic about their own ability to affect outcomes, leading
them to pass up on many productive investment opportunities. Frankenberger et al. (2007)
found that a third of poor Ethiopian families believed that destiny was the single most
important determinant of success. These families were less likely to make longer-term
investments, making them less likely to ever escape poverty.
Carefully designed messaging can help in such situations. However, campaigns that simply
tell people that their beliefs are inaccurate (for example, by reiterating that fertilizer increases
productivity) are unlikely to make much headway. In part, this is because people usually
disregard information that does not conform to their own mental model of a situation as
irrelevant to their own circumstances (“It’s not for me”). However, information or evidence
that directly targets the beliefs at the core of the flawed mental model has a better chance of
success.
25
The large gender gap in educational attainment between girls and boys in India is in part due
to the belief that there are few economic benefits from educating girls, whose primary social
function is believed to be limited to housework. Such beliefs reinforce themselves, since they
lead parents to pull girls out of school, which in turn means that their economic
opportunities in fact remain limited. But this problem is not unsolvable. Jensen (2012) finds
that providing villagers with precise information about the availability of jobs for girls with
high-school degrees and how to get such jobs causes teenage girls to stay in school longer,
makes them more likely to look for paid work, and leads to them marrying later. Perhaps
even more remarkable for an area with some of the worst gender gaps in education and
health, primary-school-age girls in the villages which received the recruitment drives were 5
percentage points more likely to be in school and weighed more than in control villages.
Parents had responded to the discovery that girls had economic prospects by investing more
in their nutrition and education.
The case of adolescent sex education in Kenya provides another instructive lesson on the
importance of tailoring messages to existing mental models. An education campaign in
Kenya, where many teenaged girls were getting pregnant by older men, sought to reduce
such pregnancies by urging girls to shun premarital sex. However, this reinforced the idea of
marriage as a desirable goal, and girls viewed getting pregnant as the most efficient way to
find a husband. The program therefore actually led teenaged girls to actively seek out older
partners for unprotected sex. On the other hand, a campaign that simply provided girls with
the information that older men were more likely to be HIV-positive reduced the number of
girls who got pregnant by older men by two-thirds. It succeeded because it addressed the
fundamental cause of such pregnancies, which was the perceived desirability of older men as
sexual partners (Dupas 2011).
4.3 Testing and Re-design
Which of these design principles is most useful in a particular situation will naturally depend
on many factors. Often, there will be several possible paths forward. Identifying the
interventions that seem most feasible and useful, rolling them out in a controlled way in a
small pilot program, and tracking outcomes will inform an iterative process of re-design.
Prototyping and a willingness to experiment and tweak are crucial. Over time, this will lead
to an intervention or a small set of interventions that are both psychologically sound and
administratively and logistically feasible. These can then be rigorously evaluated using the
tools development economists have developed over the past decade, which compare the
results of treatment groups to those of randomly chosen comparable control groups.
5. Conclusion
This paper is both a review of what is known about human behavior and how these insights
have been applied to development policy, and an attempt to show what is possible if these
insights and design principles are applied more broadly. We have tried to show how
26
behavioral insights allow us to understand why the kinds of problems policymakers in
developing countries face exist and persist, as well as allow us to design innovative, effective
solutions to those problems.
We conclude by looking ahead to how these lessons can be applied in practice, and what this
means for the way donors, researchers, and governments work. Adequately unlocking the
potential of behavioral solutions will require us to take a systematic approach to identifying
key problems, evaluating the potential impact of behavioral economics approaches, and
translating these insights into improvements in programs. This can only be achieved by
making some deep-seated changes in the way we go about applying behavioral insights in
development.
First, efforts to apply behavioral economics insights have to be built around the objective of
achieving impact at scale. This means moving away from a focus on relatively narrowly
conceived research projects and “boutique” pilots that aim to pin down a specific behavioral
insight or insights and towards a focus on existing programs or projects that seek to address
big development problems, but whose effectiveness is constrained by behaviors.
Secondly, innovation has to be embedded into the process of intervention design from the
very beginning and must run all the way through it. The goal must not be to test one or two
interventions but to design (possibly several) interventions based on careful problem analysis
and the identification of behavioral bottlenecks. This process of design should be an iterative
process that incorporates feedback from small tests carried out as part of the design itself.
Adopting this systematic approach towards diagnosis is important not just because it leads to
better solutions to the problem in question but because it provides us with diagnoses and
diagnostic techniques that can carry over to other contexts. Thus, if we find good diagnostics
that indicate that self-control plays a large role in understanding a particular behavior in one
country, we would have good reason to explore the use of these diagnostics in a different
setting. Insights and diagnoses are likely to have external validity even if particular designs do
not. In that sense, we should think of the kind of policy experimentation being described
here partly also as mechanism experiments (see Ludwig, Kling and Mullainathan 2011).
For donors, this means selecting projects where successes can be scaled. This affects the
kinds of problems chosen for experimentation. The most useful problems to work on would
affect people in countries or regions beyond the one initially chosen, for instance because
they are pervasive across a number of developing countries. There must also be reason to
believe, ex ante, that behavioral barriers are a critical reason for program goals not being
met. It also affects the choice of partners. Potential partners should reach large numbers of
people so that any successes can be scaled up in the context of an existing program. Working
with governments or large aid agencies may be more impactful than working directly with
individual researchers.
27
For researchers, this focus on impact at scale means privileging projects that build on
existing government or large-scale non-profit programs rather than collaborations with
small, boutique NGOs or service providers. It also means being willing to evaluate an
intervention that may not necessarily isolate the causal effect of a single psychology or
pathway, but of a suite of linked design innovations. And it means paying close attention to
the administrative burden or logistical requirements of any proposed solution, because these
affect whether a solution can be scaled up.
Finally, this means that governments need to be open to involving behavioral experts when
programs are first designed as well as to experimenting on existing programs. As important
is openness to exploring new (and sometimes surprising) pathways to impact that emerge in
the course of the detailed problem and behavioral analysis.
Embedding innovation into the design process itself leads to designs that have a greater
chance of success than if we proceeded to testing the first feasible and reasonable set of
ideas about how to solve a problem. Over time, a rigorous application of the approach to
program design outlined in this paper should lead to more effective, cheaper and more easily
replicable innovations. As we have seen, many policy problems can be traced in the ultimate
analysis to gaps between intentions and actions. A systematic application of behavioral
design should help close another, equally important gap: that between what policy seeks to
achieve and what it accomplishes.
28
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